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Optimization of Auto Equip Function in Role-Playing Game Based on Standard Deviation of Characters Stats using Genetic Algorithm

机译:基于遗传算法的人物统计标准差优化角色扮演游戏中的自动装备功能

摘要

Genetic algorithm is a well-known optimization solution for an unknown, complex case that cannot be solved using conventional methods.udIn Role-Playing Games (RPG), usually the main features are characters stats and equip items. Character has stats, namely strength, defense, speed, agility, life. Also, equip items that can boost characters stats. These items retrieved randomly when an enemy dead.udA problem arise when the player have so many items that we cannot choose the best. Latest items doesnt always mean best, because usually in RPGs, items dont always boost all stats equally, but often it reduces certain stat while increasing the other.udBased on this, a function is built in this research, to auto equip all items, based on the standard deviation of characters stats after equipping. The genetic algorithm will evaluate the best combination of gloves, armors and shoes. This algorithm involves the process of evaluating initial population (items combination), selection, crossover, mutation, elitism, creating new population. The algorithm stops when the best fitness is getting stable in successive 3 generations.udAfter the auto equip process, the character is getting significantly stronger compared to using default equip items, measured by the remaining life after fighting with several enemies.
机译:遗传算法是一种未知的复杂情况的众所周知的优化解决方案,使用传统方法无法解决。 ud在角色扮演游戏(RPG)中,通常的主要功能是角色统计和装备项目。角色具有属性,即力量,防御,速度,敏捷性,生命。另外,装备可以提高角色属性的物品。当敌人死亡时,这些物品会被随机取回。 ud当玩家拥有太多物品而我们无法选择最佳物品时,就会出现问题。最新的物品并不一定总是最好的,因为通常在RPG中,物品不会总是平等地提升所有属性,但是经常会降低某些属性而增加其他属性。 ud基于此,本研究构建了一个功能,可以自动装备所有物品,根据装备后角色属性的标准差。遗传算法将评估手套,盔甲和鞋子的最佳组合。该算法涉及评估初始种群(项目组合),选择,交叉,变异,精英,创造新种群的过程。当连续3代达到最佳适应状态时,算法停止运行。 ud在自动装备过程中,与使用默认装备相比,通过与多个敌人战斗后的剩余生命来衡量,角色变得越来越强大。

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    Purba Kristo Radion;

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  • 年度 2015
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